ArticleBMC cancer2021
The incidence, risk factors and predictive nomograms for early death of lung cancer with synchronous brain metastasis: a retrospective study in the SEER database.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- A Machine Learning-Based Model to Predict In-Hospital Mortality of Lung Cancer Patients: A Population-Based Study of 523,959 Cases.Advances in respiratory medicine · 2023Trial
- Forecasting the risk of early death among patients suffering from lung cancer with brain metastasis after radiotherapy using interpretable machine learning: a study based on the SEER database.Journal of thoracic disease · 2026Article
- Using the Systemic Inflammatory Response Index and the Prognostic Nutritional Index in Predicting Subsequent Bone Metastases in Patients with Non-Small Cell Lung Cancer.International journal of general medicine · 2026Article
- LASSO-based nomograms predict early death in small cell lung cancer (SCLC) patients with brain metastasis.Translational cancer research · 2025Article
- Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning.Discover oncology · 2025Article
- Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas.Frontiers in oncology · 2025Article
- Validation of a prognostic scoring system for brain metastases with synchronous detection of primary cancers at initial consultation.Frontiers in oncology · 2025Article
- Development and validation of machine learning model to predict early death of melanoma brain metastasis patients.Frontiers in oncology · 2025Article
- Incidence trends and prognostic factors in head and neck MALT lymphoma.Annals of hematology · 2025Article
- Prediction of Early Mortality in Esophageal Cancer Patients with Liver Metastasis Using Machine Learning Approaches.Life (Basel, Switzerland) · 2024Article
- Risk factors and nomograms for diagnosis and early death in patients with combined small cell lung cancer with distant metastasis: a population-based study.The Journal of international medical research · 2024Article
- Risk factors, prognostic factors, and nomograms for synchronous brain metastases of solid tumors: a population-based study.Neurosurgical review · 2024Article
- Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study.Oncology letters · 2024Article
- [Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Article
- METTL14 enhances the m6A modification level of lncRNA MSTRG.292666.16 to promote the progression of non-small cell lung cancer.Cancer cell international · 2024Article
- Clinical characteristics and outcomes of surgical resection for brain metastases from lung adenocarcinoma.Frontiers in oncology · 2024Article
- A novel tool for predicting the risk of cancer-specific early death in older patients with primary malignant melanoma of skin: a population-based analysis.Frontiers in oncology · 2024Article
- Development and validation of a nomogram for predicting overall survival in patients with stage III-N2 lung adenocarcinoma based on the SEER database.Translational cancer research · 2023Article
- A visualized dynamic prediction model for overall survival in patients diagnosed with brain metastases from lung squamous cell carcinoma.The clinical respiratory journal · 2023Article
- Development and validation of nomograms to predict early death in non-small cell lung cancer patients with brain metastasis: a retrospective study in the SEER database.Translational cancer research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prognosis of lung cancer with synchronous brain metastasis (LCBM) is very poor, and patients often die within a short time. However, little is known about the early mortality and related factors in patients with LCBM.
methodsPatients diagnosed with LCBM between 2010 and 2016 were enrolled from the Surveillance, Epidemiology, and End Result (SEER) database. Univariate and multivariate logistic regression analysis were used to identify significant independent prognostic factors, which were used to construct nomograms of overall and cancer-specific early death. Then, the prediction ability of the model was verified by receiver operating characteristic (ROC) curve. At last, the clinical application value of the model was tested through decision curve analysis (DCA).
resultsA total of 29,902 patients with LCBM were enrolled in this study. Among them, 13,275 (44.4%) patients had early death, and 11,425 (38.2%) cases died of lung cancer. The significant independent risk factors for overall and cancer-specific early death included age, race, gender, Gleason grade, histological type, T stage, N stage, bone metastasis, liver metastasis and marital status, which were used to construct the nomogram. The ROC curve demonstrated good predictive ability and clinical application value. The areas under the curve (AUC) of the training group was 0.793 (95% CI: 0.788-0.799) and 0.794 (95% CI: 0.788-0.799), in the model of overall and cancer-specific early death respectively. And the AUC of the validation group were 0.803 (95% CI: 0.788-0.818) and 0.806 (95% CI: 0.791-0.821), respectively. The calibration plots of the model showed that the predicted early death is consistent with the actual value. The DCA analysis indicated a good clinical application value of this model.
conclusionsWe established a comprehensive nomogram to predict early death in lung cancer patients with synchronous brain metastases. Nomograms may help oncologists develop better treatment strategies, such as clinical trials and hospice care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.